Vibration monitoring and diagnosing system for wind power generator

ABSTRACT

Disclosed herein is a vibration monitoring and diagnosing system for monitoring conditions of a wind power generator and diagnosing a defective portion thereof using vibration characteristics obtained from acceleration sensors mounted to the wind power generator. A vibration-based defect detecting method may include: collecting vibration data of the wind power generator using the plurality of sensors; extracting a first characteristic value of a time domain based on the vibration data; extracting characteristic values in one or more frequency bands for a location of each sensor in a frequency domain or an envelope frequency domain if the first characteristic value is greater than a preset alarm setting value; and determining that a defect is present when at least one characteristic value of the characteristic values is greater than a preset normal value.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims priority to Korean Patent Application No.10-2016-0077112, filed on Jun. 21, 2016, the disclosure of which isincorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION Field of the Invention

Exemplary embodiments of the present invention relate to a system formonitoring and diagnosing conditions of a wind power generator, and moreparticularly, to a vibration monitoring and diagnosing system formonitoring conditions of a wind power generator and diagnosing adefective portion thereof using vibration characteristics obtained fromacceleration sensors mounted to the wind power generator.

Description of the Related Art

Recently, because of exhaustion of fossil energy and environmentalissues such as climate changes and greenhouse gas mitigation,investments in development of new renewable energy is increasing, anddemand for wind power generators is globally increasing.

With spread of such energy supply, 3.4% of total world electricityconsumption in 2014 was produced by wind power generators, and it isexpected that it reaches 5.3% in 2019.

However, such wind power generators are equipment that it is difficultto manage compared to thermal or nuclear power generation equipment,because a comparatively large number of apparatuses are required perunit power generation capacity, and work for maintenance or repair iseasily affected by accessibility depending on weather conditions, supplyand demand of components and maintenance equipment, the number ofworkers, etc.

In particular, with regard to offshore wind power systems, there arerestrictions on access to the wind power systems depending on the windspeed and the height of waves. Thus, detecting malfunction of componentsin early stages and establishing a maintenance plan for preventing aserious accident are essential to reduce the maintenance cost. Toachieve the above-mentioned purpose, various systems for monitoringconditions of wind power generators have been proposed. Such systems formonitoring the conditions of the wind power generators are importantmeans which makes it possible to detect malfunction of a component in anearly stage and determine a proper time for maintenance in terms ofpredictive maintenance and state-based maintenance.

Particularly, a state monitoring system for monitoring vibrationconditions of a wind power generator and monitoring conditions of thewind power generator using the obtained vibration conditions is known asa system suitable for monitoring and diagnosing the conditions ofmechanical rotating components of the wind power generator. Recently,research on a method of monitoring and diagnosing conditions of a windpower generator become appreciably more active.

It is a question of how much effectively and reliably a proposed systemcan monitor and diagnose conditions of a wind power generator.

PRIOR ART DOCUMENT

[Patent Document] Korean Patent Registration No. 1345598 (date: Dec. 27,2013)

SUMMARY OF THE INVENTION

An object of the present invention is to provide a vibration monitoringand diagnosing system for a wind power generator which is capable ofmonitoring and diagnosing conditions of the wind power generator usingdata about vibration on a main bearing, a gearbox, and a generator whichconstitute a rotating machine of the wind power generator, thus makingit possible to effectively and reliably monitor and diagnose theconditions of the wind power generator.

Other objects and advantages of the present invention can be understoodby the following description, and become apparent with reference to theembodiments of the present invention. Also, it is obvious to thoseskilled in the art to which the present invention pertains that theobjects and advantages of the present invention can be realized by themeans as claimed and combinations thereof.

In accordance with one aspect of the present invention, avibration-based defect detecting method of detecting a defect of a windpower generator using vibration data collected from a plurality ofsensors, the vibration-based defect detecting method including:collecting vibration data of the wind power generator using theplurality of sensors; extracting a first characteristic value of a timedomain based on the vibration data; and determining whether the firstcharacteristic value is greater than a preset alarm setting value, anddetecting, when it is determined that the first characteristic value isgreater than a preset alarm setting value, a defect by extracting acharacteristic value of a frequency domain obtained by performing aFourier transform operation on the vibration data, wherein the detectingof the defect by extracting the characteristic value of the frequencydomain may include: extracting, based on the respective vibration datacollected by the plurality of sensors, second characteristic values forone or more preset frequency bands by each of the sensors in thefrequency domain; extracting, based on the respective vibration datacollected by the plurality of sensors, third characteristic values forone or more preset frequency bands by each of the sensors in an envelopefrequency domain; and determining that a defect is present when at leastone characteristic value of the second characteristic values or thethird characteristic values is a preset normal value or more. Thevibration-based defect detecting method may further include detecting,when the at least one characteristic value of the second characteristicvalues or the third characteristic values is the preset normal value ormore, a location and a kind of the defect based on a frequency band anda location of a corresponding sensor from which the at least onecharacteristic value has been extracted.

The first characteristic value includes at least one of a root meansquare, a kurtosis, and a crest factor, the root mean square (x_(rms))is calculated by

${x_{rms} = \sqrt{\frac{\sum\limits_{i = 1}^{n}x_{i}^{2}}{n}}},$

the kurtosis (x_(k)) is calculated by

${x_{k} = {\frac{{E\left( y_{i} \right)}^{4}}{\sigma^{4}} = \frac{\frac{1}{n}{\sum\limits_{i = 1}^{n}\left( {x_{i} - \overset{\_}{x}} \right)^{4}}}{\left( \sqrt{\frac{1}{n}{\sum\limits_{i = 1}^{n}\left( {x_{i} - \overset{\_}{x}} \right)^{2}}} \right)^{4}}}},$

and the crest factor (x_(c)) is calculated by

${x_{c} = \frac{x_{peak}}{x_{rms}}},$

and wherein x_(i) denotes the collected vibration data, x denotes anaverage of the vibration data, and |x_(peak)| denotes a peak value of anabsolute value of the vibration data.

The extracting of the second characteristic values for the one or morepreset frequency bands by each of the sensors in the frequency domainbased on the respective vibration data collected by the sensors mayinclude: performing a fast Fourier transform (FFT) operation based onthe vibration data for each of the sensors; and extracting, as a secondcharacteristic value based on a result of the performing of the FFToperation, a peak value in each of the one or more certain frequencybands for each location. The extracting of the third characteristicvalues for the one or more preset frequency bands by each of the sensorsin the envelope frequency domain based on the respective vibration datacollected by the sensors may include: extracting an envelope from thevibration data for each of the sensors; performing a fast Fouriertransform (FFT) operation based on the envelope; and extracting, as athird characteristic value based on a result of the performing of theFFT operation, a peak value in each of the one or more certain frequencybands for each location. The extracting of the envelope from thevibration data may include: passing the vibration data through a bandpass filter; obtaining an absolute value of an output of the band passfilter; and passing the obtained absolute value through a low passfilter. Each of the one or more certain frequency bands may include atleast one of six frequency bands each of which has, as a centerfrequency, a frequency f_(r), f_(c), f_(s), f_(o), f_(t), or GMFcalculated by Equations (1) to (6), and includes opposite ends each ofwhich is spaced apart from the center frequency by 1 Hz.

$\begin{matrix}{{f_{r} = {\frac{rmp}{60} = {rps}}},} & {{Equation}\mspace{14mu} (1)} \\{{f_{c} = {\frac{f_{r}}{2}\left\lbrack {1 - {\frac{B_{d}}{P_{d}}\cos \mspace{11mu} \varphi}} \right\rbrack}},} & {{Equation}\mspace{14mu} (2)} \\{{f_{s} = {\frac{P_{d}}{2B_{d}}{f_{r}\left\lbrack {1 - \left( {\frac{B_{d}}{P_{d}}\cos \mspace{11mu} \varphi} \right)^{2}} \right\rbrack}}},} & {{Equation}\mspace{14mu} (3)} \\{{f_{o} = {{N({FTF})} = {\frac{f_{r}}{2}{N\left\lbrack {1 - {\frac{B_{d}}{P_{d}}\cos \mspace{11mu} \varphi}} \right\rbrack}}}},} & {{Equation}\mspace{14mu} (4)} \\{{f_{i} = {{N\left( {f_{r} - {FTF}} \right)} = {\frac{f_{r}}{2}{N\left\lbrack {1 + {\frac{B_{d}}{P_{d}}\cos \mspace{11mu} \varphi}} \right\rbrack}}}},{and}} & {{Equation}\mspace{14mu} (5)} \\{{GMF} = {{\left( {T_{R} + T_{S}} \right) \times N_{O}} = {\left( {T_{R} \times N_{R}} \right) + \left( {N_{S} \times T_{S}} \right)}}} & {{Equation}\mspace{14mu} (6)}\end{matrix}$

The vibration-based defect detecting method may further include: forminga frequency matrix having respective locations of the plurality ofsensors and the one or more frequency bands as a row and a column andhaving the second characteristic values as values of the matrix, and anenvelope frequency matrix having respective locations of the pluralityof sensors and the one or more frequency bands as a row and a column andhaving the third characteristic values as values of the matrix; anddisplaying the frequency matrix and the envelope frequency matrix on adisplay.

The vibration data may be classified into a plurality of classesaccording to operation conditions. The above-mentioned operations may beperformed for each of the plurality of classes.

In accordance with another aspect of the present invention, avibration-based defect detecting system of detecting a defect of a windpower generator including a main bearing, a gearbox, and a generatorusing vibration data collected from a plurality of sensors, thevibration-based defect detecting system including: a sensor unitcomprising a plurality of sensors mounted to the wind power generatorand configured to collect the vibration data; an abnormal statedetection unit configured to extract a first characteristic value of atime domain based on the vibration data collected by the sensor unit anddetect whether the wind power generator is in an abnormal state; and aprecise diagnosis unit configured to determine, when an abnormal statedetection signal is received from the abnormal state detection unit, alocation and a kind of a defect by extracting a characteristic value ofa frequency domain obtained by performing a Fourier transform operationon the vibration data collected by the sensor unit.

The plurality of sensors may include one tachometer and fifteenacceleration sensors. The tachometer may be mounted to a driven shaftextending from the gearbox to the generator and is configured to measurean RPM of the driven shaft. The fifteen acceleration sensors may includetwo accelerations configured to measure vertical and horizontalvibrations on the main bearing, one acceleration sensor provided on eachof an left end and a right end of a torque arm of the gearbox coupledwith the main shaft, one acceleration sensor configured to measurevibration of a mechanical pump bearing, one acceleration sensorconfigured to measure vibration of a wheel bearing of a third gear stageof the gearbox, one acceleration sensor configured to measure vibrationof a drive shaft of the third gear stage of the gearbox, twoacceleration sensors configured to measure vibration of a driven shaftof the third gear stage of the gearbox, two acceleration sensorsconfigured to measure horizontal and vertical vibrations at a side ofthe generator which is coupled with the gearbox so as to measurevibration of the generator, and two acceleration sensors configured tomeasure horizontal and vertical vibrations at a side opposite to theside of the generator that is coupled with the gearbox, and twoacceleration sensors configured to collect front/rear directionvibration and left/right direction vibration of the wind powergenerator.

The abnormal state detection unit may calculate a first characteristicvalue including at least one of a root mean square, a kurtosis, and acrest factor based on the vibration data, and determines that the windpower generator is in an abnormal state when the first characteristicvalue is a preset alarm setting value or more, wherein the root meansquare (x_(rms)) is calculated by

${x_{rms} = \sqrt{\frac{\sum\limits_{i = 1}^{n}x_{i}^{2}}{n}}},$

the kurtosis (x_(k)) is calculated by

${x_{k} = {\frac{{E\left( y_{i} \right)}^{4}}{\sigma^{2}} = \frac{\frac{1}{n}{\sum\limits_{i = 1}^{n}\left( {x_{i} - \overset{\_}{x}} \right)^{4}}}{\left( \sqrt{\frac{1}{n}{\sum\limits_{i = 1}^{n}\left( {x_{i} - \overset{\_}{x}} \right)^{2}}} \right)^{4}}}},$

and the crest factor (x_(c)) is calculated by

${x_{c} = \frac{x_{peak}}{x_{rms}}},$

and wherein x_(i) denotes the collected vibration data, x denotes anaverage of the vibration data, and |x_(peak)| denotes a peak value of anabsolute value of the vibration data.

The precise diagnosis unit may perform a fast Fourier transform (FFT)based on the vibration data for the sensors collected by the respectivesensors. The precise diagnosis unit may calculate, based on a result ofthe performing of the FFT operation, second characteristic values eachof which is a peak value in each of one or more certain frequency bandsfor each location. When at least one of the second characteristic valuesis greater than a preset normal value, the precise diagnosis unit maydetermine that a defect is present, and determine a location and a kindof the defect based both on a location of a sensor that has obtained theat least one second characteristic value and on the one or more presetcenter frequencies.

The precise diagnosis unit may extract an envelope of the vibration datafor each of the plurality of sensors. The precise diagnosis unit mayperform a fast Fourier transform (FFT) operation based on the envelope.The precise diagnosis unit may calculate, based on a result of theperforming of the FFT operation, third characteristic values each ofwhich is a peak value in each of one or more certain frequency bands foreach location. When at least one of the third characteristic values isgreater than a preset normal value, the precise diagnosis unit maydetermine that a defect is present, and determine a location and a kindof the defect based both on a location of the sensor that has obtainedthe at least one third characteristic value and on the preset centerfrequency. The vibration-based defect detecting system may furtherinclude a display unit. An envelope frequency matrix, having thelocations and the one or more certain frequency bands as a row and acolumn and including the third characteristic values as values of thematrix, may be formed. The envelope frequency matrix may be displayed onthe display unit. The precise diagnosis unit may be configured toextract the envelope by passing the vibration data through a band passfilter, obtaining an absolute value of an output of the band passfilter, and passing the obtained absolute value through a low passfilter.

Each of the one or more certain frequency bands may include at least oneof six frequency bands each of which has, as a center frequency, afrequency f_(r), f_(c), f_(s), f_(o), f_(t), or GMF calculated byEquations (1) to (6), and includes opposite ends each of which is spacedapart from the center frequency by 1 Hz.

$\begin{matrix}{{f_{r} = {\frac{rpm}{60} = {rps}}},} & {{Equation}\mspace{14mu} (1)} \\{{f_{c} = {\frac{f_{r}}{2}\left\lbrack {1 - {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}},} & {{Equation}\mspace{14mu} (2)} \\{{f_{s} = {\frac{P_{d}}{2B_{d}}{f_{r}\left\lbrack {1 - \left( {\frac{B_{d}}{P_{d}}\cos \; \varphi} \right)^{2}} \right\rbrack}}},} & {{Equation}\mspace{14mu} (3)} \\{{f_{o} = {{N\left( {F\; T\; F} \right)} = {\frac{f_{r}}{2}{N\left\lbrack {1 - {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}}}},} & {{Equation}\mspace{14mu} (4)} \\{{f_{i} = {{N\left( {f_{r} - {F\; T\; F}} \right)} = {\frac{f_{r}}{2}{N\left\lbrack {1 + {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}}}},{and}} & {{Equation}\mspace{14mu} (5)} \\{{G\; M\; F} = {{\left( {T_{R} + T_{S}} \right) \times N_{O}} = {\left( {T_{R} \times N_{R}} \right) + \left( {N_{S} \times T_{S}} \right)}}} & {{Equation}\mspace{14mu} (6)}\end{matrix}$

As described above, the present invention provides an effective andreliable state monitoring and diagnosis system based on vibrationcharacteristics of a wind power generator, thus making it possible foran operator to detect a malfunction of a component in early stage andestablish a maintenance plan for preventing a serious accident.

It is to be understood that both the foregoing general description andthe following detailed description of the present invention areexemplary and explanatory and are intended to provide furtherexplanation of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and other advantages of thepresent invention will be more clearly understood from the followingdetailed description taken in conjunction with the accompanyingdrawings, in which:

FIG. 1 is a diagram illustrating positions of sensors for monitoringconditions of vibration of a wind power generator according to anembodiment of the present invention.

FIG. 2 is a flowchart showing a vibration monitoring and diagnosingmethod of a vibration monitoring and diagnosing system for the windpower generator according to an embodiment of the present invention.

FIG. 3 is a flowchart showing a frequency domain analysis processaccording to an embodiment of the present invention.

FIG. 4 is a diagram illustrating a frequency characteristic obtained byperforming an FFT operation for vibration data and a method ofextracting a narrowband peak according to an embodiment of the presentinvention.

FIG. 5 is a diagram illustrating parameters for calculating a defectfrequency of a bearing.

FIG. 6 is a diagram showing parameters for calculating a GMF of aplanetary gear of an accelerating unit 120.

FIG. 7 is a flowchart showing an envelope frequency domain analysisprocess according to an embodiment of the present invention.

FIG. 8 is a diagram illustrating an embodiment showing a frequencymatrix and an envelope frequency matrix according to the presentinvention.

FIG. 9 is a schematic block diagram of a system of detecting avibration-based defect of the wind power generator according to anembodiment of the present invention.

FIG. 10 is a diagram illustrating an embodiment of displaying ananalysis result on a display unit 940 according to the presentinvention.

FIG. 11 is a diagram illustrating another embodiment of displaying ananalysis result on the display unit 940 according to the presentinvention.

DESCRIPTION OF SPECIFIC EMBODIMENTS

Hereinafter, embodiments of the present invention will be described indetail with reference to the attached drawings, such that those skilledin the art can easily implement the present invention. The presentinvention may be embodied in various different forms without beinglimited to the following embodiments.

Furthermore, in the drawings, portions which are not related to thepresent invention will be omitted to explain the present invention moreclearly. Reference should be made to the drawings, in which similarreference numerals are used throughout the different drawings todesignate similar components.

It will be understood that when an element is referred to as being“coupled” or “connected” to another element, it can be directly coupledor connected to the other element or intervening elements may be presenttherebetween. In addition, when an element is referred to as“comprising” or “including” a component, it does not preclude anothercomponent but may further include the other component unless the contextclearly indicates otherwise.

It will be understood that when an element is referred to as being “on”another element, it can be directly on another element or interveningelements may be present therebetween. In contrast, when an element isreferred to as being “directly on” another element, there are nointervening elements therebetween.

It will be understood that, although the terms first, second, third,etc. may be used herein to describe various elements, components,regions, layers and/or sections, but are not limited thereto. Theseterms are only used to distinguish one element, component, region,layer, or section from another element, component, region, layer, orsection. Therefore, a first element, component, region, layer, orsection discussed below could be termed a second element, component,region, layer, or section without departing from the teachings of thepresent invention.

The technical terms used in the present specification are set forth tomention a specific embodiment of the present invention, and do notintended to define the scope of the present invention. As used herein,the singular forms “a”, “an” and “the” are intended to include theplural forms as well, unless the context clearly indicates otherwise. Inthe present specification, the term “including” is intended to embodyspecific properties, regions, integers, steps, operations, elementsand/or components, but is not intended to exclude presence or additionof other properties, regions, integers, steps, operations, elements,components and/or groups.

Spatially relative terms, such as “below”, “above”, and the like, may beused herein for ease of description to describe one element or feature'srelationship to another element(s) or feature(s) as illustrated in thefigures. It will be understood that the spatially relative terms areintended to encompass different orientations of the device in use oroperation in addition to the orientation depicted in the drawings. Forexample, if the device in the figures is turned over, elements describedas “below” other elements or features would then be oriented “above” theother elements or features. Therefore, the exemplary term “below” canencompass both an orientation of above and below. Devices may beotherwise rotated 90 degrees or at other angles and the spatiallyrelative descriptors used herein interpreted accordingly.

Unless otherwise defined, all terms including technical and scientificterms used herein have the same meaning as commonly understood by one ofordinary skill in the art to which this invention belongs. It will befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art and thepresent disclosure, and will not be interpreted in an idealized oroverly formal sense unless expressly so defined herein.

A wind power generator is equipment designed and manufactured to producethe optimum output taking into account changes in speed and direction ofwind. To realize the optimum control, the wind power generator mayperform not only torque control but also pitch and yaw control using aprogrammable logic controller. Generally, in the wind power generator,there may be a constant speed section and a variable speed section inwhich rotating speeds of a rotor and a gearbox shaft vary within a rangefrom a value corresponding to a cut-in wind speed of 3 m/s to a valuecorresponding to a cut-out wind speed of 25 m/s.

FIG. 1 is a diagram illustrating positions of sensors for monitoringconditions of vibration of a wind power generator according to anembodiment of the present invention.

Referring to FIG. 1, to monitor conditions of vibration of the windpower generator, a plurality of acceleration sensors capable of sensinga change in speed per time, and a laser tachometer capable of measuringthe RPM of an object may be provided. The sensors may be installed in ahousing at positions adjacent to a main bearing 110, a gearbox 120, anda generator 130 of the wind power generator. In the wind power generatorwhich is a structure for generating power using rotation of a mainshaft, the main bearing 110 is a part which supports the weight of thestructure and makes the rotation of the main shaft possible. The mainbearing 110 is an important element in terms not only of operationalperformance of rotational equipment but also of maintenance thereof. Thegearbox 120 is a device which uses gears and increases the rotatingspeed of energy supplied from an impeller of the wind power generator toset the rotating speed to a value suitable for generating power. Thegearbox 120 introduced in an embodiment of the present invention has athree-stage structure in which each of first and second gear stagespertain to a planetary gear structure and a third gear stage pertains toa helical gear structure. A drive shaft of the gearbox 120 is coupled toa planetary gear of the second gear stage, and a driven shaft is coupledto the generator 130 so that the rotating speed of energy to be input tothe generator 130 can be increased by the gearbox 120. The generator 130is a device which generates power using input rotational energy. Themain bearing 110, the gearbox 120, and the generator 130 may be keycomponents of the wind power generator.

In more detail, referring to FIG. 1, the wind power generator accordingto the embodiment of the present invention may be provided with fifteenacceleration sensors and one tachometer to monitor conditions ofvibration. The tachometer may be mounted to the driven shaft of thehelical gear so as to measure the RPM of the driven shaft. The measuredRPM may be the same as the RPM of energy to be inputted to thegenerator. The fifteen acceleration sensors may be divided into nineacceleration sensors for measuring vertical acceleration, and sixacceleration sensors for measuring horizontal acceleration. Eachacceleration sensor may sense horizontal vibration or verticalvibration. The acceleration sensors may be mounted to housings of thecorresponding main components. Two acceleration sensors may be mountedto the main bearing 110 respectively in horizontal and verticaldirections so as to measure vertical and horizontal vibrations. Sevenacceleration sensors may be mounted to the gearbox 120. One accelerationsensor may be mounted to each of left and right ends of a torque arm ofthe gear box 120 which is coupled with the main shaft. One accelerationsensor may be mounted to a mechanical pump bearing. One accelerationsensor may be mounted to a wheel bearing of the third gear stage of thegearbox. One acceleration sensor may be mounted to the drive shaft ofthe third gear stage of the gearbox, and two acceleration sensors may bemounted to the driven shaft of the third gear stage of the gearbox. Fouracceleration sensors may be horizontally and vertically mounted to thegenerator 130, wherein two acceleration sensors may be horizontally andvertically mounted to a first side of the generator 130 which is coupledwith the gearbox 120, and two acceleration sensors may be horizontallyand vertically mounted to a second side thereof opposite to the firstside. The last two acceleration sensors may be mounted to the housing ofthe wind power generator so as to measure front/rear direction vibrationand left/right direction vibration of the wind power generator.

The fifteen acceleration sensors mounted to the wind power generator inthe above-mentioned manner and the one tachometer may be used to obtainvibration data for respective parts of the wind power generator.

As shown in Table 1, the obtained vibration data may be classified intoa plurality of classes according to a turbine operating region of thewind power generator before being stored. Here, class 1 may be avariable speed section, classes 2 to 5 may be constant speed sections.The term “third gear stage of gearbox” may mean the RPM of the driveshaft of the third gear stage of the gearbox which is input to thegenerator 130, as described above.

TABLE 1 Rotating speed Change in of third gear rotating Time Producedstage of gearbox speed delay power (P) Pitch/Yaw Class [rpm] [rpm] [s][MW] movement 1 700~900 15 20 — —/— 2 1400~1550 50 15 1.6 < P < 2.0 —/—3 1400~1550 50 15 2.0 < P < 3.0 —/— 4 1400~1550 50 15 P≧3.0 Off/Off 51400~1550 50 15 P≧3.0 —/—

To collect defect data representing deterioration in the performance ofthe wind power generator, there is the need of separately extractingvalid characteristics referring to characteristics of defects in thevariable speed section and the constant speed section. Such a validcharacteristic referring to a defect may be represented by a defectfrequency. With regard to characteristics of the defect frequency,statistical calculation in a time domain is performed, and, in afrequency domain, a defective portion may be detected by comparing, withmathematically calculated frequency information, a defect frequency suchas a gear mesh frequency (GMF) which is a frequency generated byengagement between the main bearing 110 and a gear.

FIG. 2 is a flowchart showing a vibration monitoring and diagnosingmethod of a vibration monitoring and diagnosing system for the windpower generator according to an embodiment of the present invention.

Referring to FIG. 2, the entire monitoring and diagnosing method may bedivided into a continuous health detection (CHD) operation S200, and anevent health detection operation S300. In the continuous healthdetection operation S200, vibration data is analyzed in the time domainso that a statistical characteristic value is extracted to check defectconditions of the wind power generator. In the event health detectionoperation S300, when it is determined that a defect is present in thecontinuous health detection operation S200, precise diagnosis fordetermining the location and the kind of the generated defect may beperformed by analysis in the frequency domain and an envelope frequencydomain.

The continuous health detection operation S200 may include operationS180 of collecting vibration data from the sensors, operation S220 ofclassifying the collected vibration data into the classes according tooperation conditions shown in Table 1, and operation S230 of extractinga characteristic value of the time domain from the vibration data foreach vibration location for each class. If the extracted characteristicvalue is greater than an alarm setting value, it is determined thatthere is a defect, in operation S240, so that the process enters theeven health detection operation S300. If the extracted characteristicvalue is the alarm setting value or less, it is determined that the windpower generator is in a normal state, so that the continuous healthdetection operation S200 is continuously performed.

The characteristic values extracted in the continuous health detectionoperation S200 may include a root mean square, kurtosis, and a crestfactor. The root mean square may be calculated by Equation 1, thekurtosis may be calculated by Equation 2, and the crest factor may becalculated by Equation 3.

$\begin{matrix}{x_{rms} = \sqrt{\frac{\sum\limits_{i = 1}^{n}x_{i}^{2}}{n}}} & {{Equation}\mspace{14mu} (1)} \\{x_{k} = {\frac{{E\left( y_{i} \right)}^{4}}{\sigma^{4}} = \frac{\frac{1}{n}{\sum\limits_{i = 1}^{n}\left( {x_{i} - \overset{\_}{x}} \right)^{4}}}{\left( \sqrt{\frac{1}{n}{\sum\limits_{i = 1}^{n}\left( {x_{i} - \overset{\_}{x}} \right)^{2}}} \right)^{4}}}} & {{Equation}\mspace{14mu} (2)} \\{x_{c} = \frac{x_{peak}}{x_{rms}}} & {{Equation}\mspace{14mu} (3)}\end{matrix}$

Here, x_(i) denotes collected vibration data, x denotes an average ofthe vibration data, and |x_(peak)| denotes the peak value of theabsolute value of the vibration data. The alarm setting value may be setwith reference to ISO 10816-3 standard or VDI3834-1 standard.

In the event health detection operation S300, the precise diagnosiscapable of determining the defective portion can be performed in such away that a frequency domain analysis operation S310 and an envelopefrequency domain analysis operation S340 are performed, and it ischecked whether vibration in a defect frequency to be calculated by thefollowing equations 4 to 9 has a value greater than a normal value.

FIG. 3 is a flowchart showing a frequency domain analysis processaccording to an embodiment of the present invention.

Referring to FIG. 3, the frequency domain analysis operation S310 may beseparately performed for each of the classes classified in the CHDoperation. To perform the frequency domain analysis operation S310, afast Fourier transform (FFT) operation S311 is performed using vibrationdata for each class. In operation S313, a narrowband peak extractionmethod may be used to extract the peak value in each narrowband. Basedon this, a frequency matrix may be formed, in operation S315. Here, thedefect determination operation may be performed using the extracted peakvalue, and the frequency matrix may be used to easily display anassociated defective region on a display later.

FIG. 4 is a diagram illustrating a frequency characteristic obtained byperforming an FFT operation for vibration data and a method ofextracting a narrowband peak according to an embodiment of the presentinvention.

The method of extracting a narrowband peak will be described in moredetail with reference to FIG. 4. The method of extracting the narrowbandpeak is a method in which a frequency calculated by Equations 4 to 9 isused as a center frequency, a band having opposite ends, each of whichis spaced apart from the center frequency by 1 Hz, and the peak value isextracted from the band.

$\begin{matrix}{f_{r} = {\frac{rpm}{60} = {rps}}} & {{Equation}\mspace{14mu} (4)} \\{{F\; T\; F} = {f_{c} = {\frac{f_{r}}{2}\left\lbrack {1 - {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}}} & {{Equation}\mspace{14mu} (5)} \\{{B\; S\; F} = {f_{s} = {\frac{P_{d}}{2B_{d}}{f_{r}\left\lbrack {1 - \left( {\frac{B_{d}}{P_{d}}\cos \; \varphi} \right)^{2}} \right\rbrack}}}} & {{Equation}\mspace{14mu} (6)} \\{{B\; P\; F\; O} = {f_{o} = {{N\left( {F\; T\; F} \right)} = {\frac{f_{r}}{2}{N\left\lbrack {1 - {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}}}}} & {{Equation}\mspace{14mu} (7)} \\{{B\; P\; F\; I} = {f_{i} = {{N\left( {f_{r} - {F\; T\; F}} \right)} = {\frac{f_{r}}{2}{N\left\lbrack {1 + {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}}}}} & {{Equation}\mspace{14mu} (8)} \\{{G\; M\; F} = {{\left( {T_{R} + T_{S}} \right) \times N_{O}} = {\left( {T_{R} \times N_{R}} \right) + \left( {N_{S} \times T_{S}} \right)}}} & {{Equation}\mspace{14mu} (9)}\end{matrix}$

Equations 4 to 8 are provided to calculate a defect frequency of thebearing. Here, f_(r) denotes an RPM of a shaft, f_(c) denotes afundamental train frequency (FTF), f_(s) denotes a ball spin frequency(BSF), f_(o) denotes a ball pass frequency of an outer ring (BPFO),f_(t) and denotes a ball pass frequency of an inner ring (BPFI). Withregard to the parameters used in the foregoing equations, referring toFIG. 5, B_(d) denotes a diameter of a bearing ball, P_(d) denotes apitch diameter, N denotes the number of balls, and φ denotes a contactangle. Equation 9 is an equation for calculating a GMF of a planetarygear of the gearbox 120. With regard to the parameters used in thisequation, referring to FIG. 6, T_(R) denotes the number of teeth of aring gear 510, T_(S) denotes the number of teeth of a sun gear 520,N_(O) denotes an RPM of a carrier 530, and N_(S) denotes an RPM of a sungear 520.

FIG. 7 is a flowchart showing an envelope frequency domain analysisprocess according to an embodiment of the present invention.

Referring to FIG. 7, to perform the envelope frequency domain analysisoperation S340, an envelope using vibration data for each class may beextracted, in operation S341. The envelope may be extracted by passingthe vibration data through a band pass filter, extracting the absolutevalue thereof, and performing a low pass filter processing operation. Inoperation S343, an FFT operation on the extracted envelope is performed,and a peak value for each narrowband is extracted by a narrowband peakextraction method in operation S345. Based on this, an envelopefrequency matrix may be formed, in operation S347. Here, the defectdetermination operation may be performed using the extracted peak value,and the envelope frequency matrix may be used to easily display anassociated defective region on a display later.

FIG. 8 is a diagram illustrating an embodiment showing a frequencymatrix and an envelope frequency matrix according to the presentinvention.

Referring to FIG. 8, a column 810 of each of the frequency matrix andthe envelope frequency matrix denotes a vibration measurement location,and a row 820 thereof denotes a frequency band calculated by Equations 4to 6. The peak value extracted by the above-described narrowband peakextraction method refers to a value of the matrix.

Referring again to FIG. 2, in operations S320 and S350, it is determinedwhether a defect is present by analyzing each of the frequency domainand the envelope frequency domain and comparing the peak value obtainedin certain frequency bands calculated by Equations 4 to 9 at each sensorlocation with the preset normal value. If a defect is present, thelocation and the kind of the defect may be detected based on thefrequency band and the sensor location at which the peak point isobtained, in operations S330 and S360. If, in the frequency matrix orthe envelope frequency matrix, the portion in which the defect ispresent is indicated with a color or a hash different from that of theother portion in which there is no defect, an operator can more easilyrecognize whether a defect is present, and the location and the kind ofthe defect. Referring to an example of FIG. 8, if an obtained vibrationpeak value 830 is greater than the preset normal value, it is determinedthat a defect is present, and the peak value may be indicated with a redcolor. Furthermore, the operator can easily recognize that the locationat which the peak value is obtained corresponds to the gearbox, and thefrequency band pertains to the outer ring. Therefore, in the case of theexample of FIG. 8, it can be easily recognized that the defect ispresent in the outer ring of the gearbox.

Thereafter, the defect detected in the frequency domain and the defectdetected in the envelope frequency domain are double-checked. In thecase where the defect is detected in both domains, it can be determinedthat the probability of an actual presence of the defect is markedlyincreased.

FIG. 9 is a schematic block diagram of a system of detecting avibration-based defect of the wind power generator according to anembodiment of the present invention.

Referring to FIG. 9, the system of detecting a vibration-based defect ofthe wind power generator may include a sensor unit 910, an abnormalstate detection unit 920, a precise diagnosis unit 930, and a displayunit 940.

The sensor unit 910 may include acceleration sensors and a tachometerwhich are mounted to the main parts of the wind power generator, asshown in FIG. 1. The acceleration sensors and the tachometer may collectvibration data of the main parts of the wind power generator and storethe collected vibration data in a device for detecting a defect of thewind power generator. In more detail, the tachometer may be mounted tothe driven shaft of the helical gear so as to measure the RPM of thedriven shaft. The measured RPM may be the same as the RPM of energy tobe inputted to the generator. The fifteen acceleration sensors may bedivided into nine acceleration sensors for measuring verticalacceleration, and six acceleration sensors for measuring horizontalacceleration. Each acceleration sensor may sense horizontal vibration orvertical vibration. The acceleration sensors may be mounted to thehousings of the corresponding main components. Two acceleration sensorsmay be mounted to the main bearing 110 respectively in horizontal andvertical directions so as to measure vertical and horizontal vibrations.Seven acceleration sensors may be mounted to the gearbox 120. Oneacceleration sensor may be mounted to each of left and right ends of atorque arm of the gear box 120 which is coupled with the main shaft. Oneacceleration sensor may be mounted to a mechanical pump bearing. Oneacceleration sensor may be mounted to a wheel bearing of the third gearstage of the gearbox. One acceleration sensor may be mounted to thedrive shaft of the third gear stage of the gearbox, and two accelerationsensors may be mounted to the driven shaft of the third gear stage ofthe gearbox. Four acceleration sensors may be horizontally andvertically mounted to the generator 130, wherein two accelerationsensors may be horizontally and vertically mounted to a first side ofthe generator 130 which is coupled with the gearbox 120, and twoacceleration sensors may be horizontally and vertically mounted to asecond side thereof opposite to the first side. The last twoacceleration sensors may be mounted to the housing of the wind powergenerator so as to measure front/rear direction vibration and left/rightdirection vibration of the wind power generator.

The abnormal state detection unit 920 detects whether the wind powergenerator is in an abnormal state or not by extracting characteristicvalues such as a root mean square, kurtosis, and a crest factor, whichcan be statistically obtained by Equations 1 to 3, from vibration datacollected by the sensor unit 910, and then checking whether thecharacteristic values are alarm setting values or more. If an abnormalstate is detected, the abnormal state detection unit 920 transmits asignal to the precise diagnosis unit 930 so that the precise diagnosisunit 930 can perform a subsequent process.

If a signal that an abnormal state has been detected is received fromthe abnormal state detection unit 920, the precise diagnosis unit 930may perform a precise diagnosis operation of extracting the location atwhich the defect has occurred and the kind of defect through thefrequency domain analysis or the envelope frequency domain analysis. Thedisplay unit 940 may display the characteristic values obtained from theabnormal state detection unit, and the frequency matrix and the envelopefrequency matrix which are the results of the analysis on the frequencydomain and the envelope frequency domain that are obtained from theprecise diagnosis unit.

FIG. 10 is a diagram illustrating an embodiment of displaying ananalysis result on the display unit 940 according to the presentinvention.

Referring to FIG. 10, the X-axis of the display may represent thelocations of the sensors included in the sensor unit 910, and the Y-axismay represent characteristic values in the time domain and the frequencydomain. Furthermore, according to the operation domain, data may beclassified into classes, as shown in Table 1, and the classes may bearranged along the Z-axis. In addition, the original vibration datacollected by the sensors of the sensor unit 910 may also displayed onthe display unit 940. In detail, the root mean square, the kurtosis, andthe crest factor of each sensor may be displayed in the time domain 1010based on the location of each sensor and the data collected from thesensor. The frequency matrix and the envelope frequency matrix obtainedby the frequency domain analysis operation S310 and the envelopefrequency domain analysis operation S320 of the above-mentioned eventhealth detection operation S300 may be displayed in the frequency andenvelope frequency domains 1020. The vibration data domain 1030 maydisplay the vibration data substantially obtained from the sensors as itis. Particularly, in the case where an abnormal state is detected, thevalue of the defect is displayed with a color or a hash different fromthat of the other portions, thus allowing the user to easily recognizethe abnormal state.

FIG. 11 is a diagram illustrating another embodiment of displaying ananalysis result on the display unit 940 according to the presentinvention.

Referring to FIG. 11, the display unit 940 may include a fundamentalinformation unit 1110, a gear unit 1120, and a bearing unit 1130.

The fundamental information unit 1110 may display fundamentalinformation about the wind power generator. The fundamental informationmay include information about a site where the wind power generator isinstalled, summary information including information about the timeduring which the vibration data is collected, operation data and filterinformation including a wind speed, a generator speed, generatorproduction power, etc. of the wind power generator which is currentlyoperated, and a shock finder configured to indicate, based on a presetfilter range, the number of sensors which undergo vibrationcorresponding to the filter range.

Each of the gear unit 1120 and the bearing unit 1130 may include acolumn 1121, 1131 which represents installation locations of vibrationsensors, a part 1123, 1133 which represents characteristic values ofvibration data by each vibration sensor, and a part 1125, 1135 whichsets a filter value. Since the gear unit 1120 and the bearing unit 1130may differ from each other in vibration data, there is a need fordisplaying the characteristic values of the vibration data of respectivedifferent regions of the display unit 940. In particular, among thecharacteristic values of the vibration data by the vibration sensors, acharacteristic value corresponding to the preset filter value isdisplayed with a color or a hash distinguished from that of the othervalues, as designated by reference numerals 1127 and 1137, thus allowingthe user to easily recognize the location at which the defect ispresent, and the kind of defect corresponding to the center frequency.

As described above, the present invention measures and analyzesvibrations on main regions of a wind power generator, thus making itpossible to precisely diagnose a defect, thereby detecting malfunctionof a component in early stages, and preventing a serious accident.

While the present invention has been described with reference to theparticular illustrative embodiments, it is not to be restricted by theembodiments but only by the appended claims. It is to be appreciatedthat those skilled in the art can change or modify the embodimentswithout departing from the scope and spirit of the present invention.

What is claimed is:
 1. A fault detecting method of detecting a fault inof a wind power generator using vibration data, the fault detectingmethod comprising: receiving vibration data of the wind power generatorfrom a plurality of sensors; extracting a first characteristic value ina time domain based on the vibration data; determining whether the firstcharacteristic value is greater than an alarm setting value; anddetermining, when the first characteristic value is determined to begreater than the alarm setting value, whether there is the fault byextracting a characteristic value in a frequency domain obtained byperforming a Fourier transform operation on the vibration data, whereinthe determining whether there is the fault comprises: extracting, basedon the vibration data received by the plurality of sensors, secondcharacteristic values in the frequency domain, for one or more presetfrequency bands by each of the plurality of sensors; extracting, basedon the vibration data received by the plurality of sensors, thirdcharacteristic values in an envelope frequency domain for the one ormore preset frequency bands by each of the plurality of sensors in anenvelope frequency domain; and determining that the fault is presentwhen at least one from among a characteristic value of the secondcharacteristic values or a characteristic value of the thirdcharacteristic values is equal to or greater than a preset normal value.2. The fault detecting method according to claim 1, further comprisingdetermining, when the at least one from among the characteristic valueof the second characteristic values or the characteristic value of thethird characteristic values is equal to or greater than the presetnormal value, a location of the fault and a kind of the fault based on afrequency band of the preset frequency bands and a location of acorresponding sensor of the plurality of sensors.
 3. The fault detectingmethod according to claim 1, wherein the first characteristic valueincludes at least one of a root mean square, a kurtosis, and a crestfactor, wherein the root mean square (x_(rms)) is calculated by${x_{rms} = \sqrt{\frac{\sum\limits_{i = 1}^{n}x_{i}^{2}}{n}}},$ thekurtosis (x_(k)) is calculated by${x_{k} = {\frac{{E\left( y_{i} \right)}^{4}}{\sigma^{2}} = \frac{\frac{1}{n}{\sum\limits_{i = 1}^{n}\left( {x_{i} - \overset{\_}{x}} \right)^{4}}}{\left( \sqrt{\frac{1}{n}{\sum\limits_{i = 1}^{n}\left( {x_{i} - \overset{\_}{x}} \right)^{2}}} \right)^{4}}}},$ and the crest factor (x_(c)) is calculated by${x_{c} = \frac{x_{peak}}{x_{rms}}},$  and wherein x_(i) is thevibration data, x is an average of the vibration data, and |x_(peak)| isa peak value of an absolute value of the vibration data.
 4. The faultdetecting method according to claim 1, wherein the extracting of thesecond characteristic values in the frequency domain for the one or morepreset frequency bands by each of the plurality of sensors, based on thevibration data received by the plurality of sensors comprises:performing a fast Fourier transform (FFT) operation on the vibrationdata; and extracting, as a second characteristic value based on a resultof the performing of the FFT operation, a peak value in a frequency bandwhich has a corresponding one of the one or more preset centerfrequencies for each of the plurality of sensors as a center thereof,and opposite ends which are each spaced apart from the preset centerfrequency by 1 Hz.
 5. The fault detecting method according to claim 4,wherein the one or more preset center frequencies comprise frequenciesf_(r), f_(c), f_(s), f_(o), f_(t), and a gear mesh frequency (GMF)calculated by Equations (1) to (6), wherein${{{Equation}\mspace{14mu} (1)\mspace{14mu} {is}\mspace{14mu} f_{r}} = \frac{rpm}{60}},{{{Equation}\mspace{14mu} (2)\mspace{14mu} {is}\mspace{14mu} f_{c}} = {\frac{f_{r}}{2}\left\lbrack {1 - {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}},{{{Equation}\mspace{14mu} (3)\mspace{14mu} {is}\mspace{14mu} f_{s}} = {\frac{P_{d}}{2B_{d}}{f_{r}\left\lbrack {1 - \left( {\frac{B_{d}}{P_{d}}\cos \; \varphi} \right)^{2}} \right\rbrack}}},{{{Equation}\mspace{14mu} (4)\mspace{14mu} {is}\mspace{14mu} f_{o}} = {{N\left( {F\; T\; F} \right)} = {\frac{f_{r}}{2}{N\left\lbrack {1 - {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}}}},{{{Equation}\mspace{14mu} (5)\mspace{14mu} {is}\mspace{14mu} f_{i}} = {{N\left( {f_{r} - {F\; T\; F}} \right)} = {\frac{f_{r}}{2}{N\left\lbrack {1 + {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}}}},{and}$Equation  (6)  is  G M F = (T_(R) + T_(S)) × N_(O) = (T_(R) × N_(R)) + (N_(S) × T_(S)),wherein, f_(r) is revolutions per second of a shaft, f_(c) is afundamental train frequency (FTF), f_(s) is a ball spin frequency (BSF),f_(o) is a ball pass frequency of an outer ring (BPFO), f_(t) is a ballpass frequency of an inner ring (BPFI), B_(d) is a diameter of a bearingball, P_(d) is a pitch diameter, N is a number of balls, φ is a contactangle, T_(R) is a number of teeth of a ring gear, T_(S) is a number ofteeth of a sun gear, N_(O) is revolutions per second of a carrier, N_(R)is revolutions per second of the ring gear, and N_(S) is revolutions persecond of the sun gear.
 6. The fault detecting method according to claim1, wherein the extracting of the third characteristic values in theenvelope frequency domain, for the one or more preset frequency bands byeach of the plurality of sensors in the envelope frequency domain basedon the vibration data received by the sensors comprises: extracting anenvelope from the vibration data for each of the plurality of sensors;performing a fast Fourier transform (FFT) operation based on theenvelope; and extracting, as a third characteristic value based on aresult of the performing of the FFT operation, a peak value in afrequency band which has a corresponding one of the one or more presetcenter frequencies for each of the plurality of sensors as a centerthereof, and opposite ends which are each spaced apart from the presetcenter frequency by 1 Hz.
 7. The fault detecting method according toclaim 6, wherein the extracting of the envelope from the vibration datacomprises: passing the vibration data through a band pass filter;obtaining an absolute value of an output of the band pass filter; andpassing the obtained absolute value through a low pass filter.
 8. Thefault detecting method according to claim 6, wherein the one or morepreset center frequencies comprise frequencies f_(r), f_(c), f_(s),f_(o), f_(t), and a gear mesh frequency (GMF) calculated by Equations(1) to (6), wherein${{{Equation}\mspace{14mu} (1)\mspace{14mu} {is}\mspace{14mu} f_{r}} = \frac{rpm}{60}},{{{Equation}\mspace{14mu} (2)\mspace{14mu} {is}\mspace{14mu} f_{c}} = {\frac{f_{r}}{2}\left\lbrack {1 - {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}},{{{Equation}\mspace{14mu} (3)\mspace{14mu} {is}\mspace{14mu} f_{s}} = {\frac{P_{d}}{2B_{d}}{f_{r}\left\lbrack {1 - \left( {\frac{B_{d}}{P_{d}}\cos \; \varphi} \right)^{2}} \right\rbrack}}},{{{Equation}\mspace{14mu} (4)\mspace{14mu} {is}\mspace{14mu} f_{o}} = {{N\left( {F\; T\; F} \right)} = {\frac{f_{r}}{2}{N\left\lbrack {1 - {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}}}},{{{Equation}\mspace{14mu} (5)\mspace{14mu} {is}\mspace{14mu} f_{i}} = {{N\left( {f_{r} - {F\; T\; F}} \right)} = {\frac{f_{r}}{2}{N\left\lbrack {1 + {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}}}},{and}$Equation  (6)  is  G M F = (T_(R) + T_(S)) × N_(O) = (T_(R) × N_(R)) + (N_(S) × T_(S)),wherein, f_(r) is revolutions per seconds of a shaft, f_(c) is afundamental train frequency (FTF), f_(s) is a ball spin frequency (BSF),f_(o) is a ball pass frequency of an outer ring (BPFO), f_(t) is a ballpass frequency of an inner ring (BPFI), B_(d) is a diameter of a bearingball, P_(d) is a pitch diameter, N is a number of balls, φ is a contactangle, T_(R) is a number of teeth of a ring gear, T_(S) is a number ofteeth of a sun gear, N_(O) is revolutions per second of a carrier, N_(R)is revolutions per second of the ring gear, and N_(S) is revolutions persecond of the sun gear.
 9. The fault detecting method according to claim1, further comprising: forming a frequency matrix having respectivelocations of the plurality of sensors and the one or more presetfrequency bands as a row and a column and having the secondcharacteristic values as values of the matrix, and an envelope frequencymatrix having respective locations of the plurality of sensors and thepreset frequency bands as a row and a column and having the thirdcharacteristic values as values of the matrix; and displaying thefrequency matrix and the envelope frequency matrix on a display.
 10. Thefault detecting method according to claim 1, wherein the vibration datais classified into a plurality of classes according to operationconditions, and wherein operations are performed for each of theplurality of classes.
 11. A fault detecting system of detecting a faultin a wind power generator including a main bearing, a gearbox, and agenerator using vibration data received from a plurality of sensors, thefault detecting system comprising: a sensor unit comprising a pluralityof sensors disposed at the wind power generator, the plurality ofsensors being configured to receive the vibration data; at least oneprocessor configured to implement: an abnormal state detection unitconfigured to extract a first characteristic value in a time domainbased on the vibration data received by the sensor unit and detectwhether the wind power generator is in an abnormal state; and a precisediagnosis unit configured to determine, when an abnormal state detectionsignal is received from the abnormal state detection unit, a locationand a kind of a fault by extracting a characteristic value in afrequency domain obtained by performing a Fourier transform operation onthe vibration data received by the sensor unit.
 12. The fault detectingsystem according to claim 11, wherein the abnormal state detection unitcalculates a first characteristic value including at least one of a rootmean square, a kurtosis, and a crest factor based on the vibration data,and determines that the wind power generator is in the abnormal statewhen the first characteristic value is a preset alarm setting value ormore, wherein the root mean square (x_(rms)) is calculated by${x_{rms} = \sqrt{\frac{\sum\limits_{i = 1}^{n}x_{i}^{2}}{n}}},$ thekurtosis (x_(k)) is calculated by${x_{k} = {\frac{{E\left( y_{i} \right)}^{4}}{\sigma^{2}} = \frac{\frac{1}{n}{\sum\limits_{i = 1}^{n}\left( {x_{i} - \overset{\_}{x}} \right)^{4}}}{\left( \sqrt{\frac{1}{n}{\sum\limits_{i = 1}^{n}\left( {x_{i} - \overset{\_}{x}} \right)^{2}}} \right)^{4}}}},$ and the crest factor (x_(c)) is calculated by${x_{c} = \frac{x_{peak}}{x_{rms}}},$  and wherein x_(i) is thevibration data, x is an average of the vibration data, and |x_(peak)| isa peak value of an absolute value of the vibration data.
 13. The faultdetecting system according to claim 11, wherein the precise diagnosisunit performs a fast Fourier transform (FFT) based on the vibrationdata, wherein the precise diagnosis unit calculate, based on a result ofperforming the FFT, second characteristic values each of which is a peakvalue in a frequency band which has a corresponding one of the one ormore preset center frequencies for each of the plurality of sensors as acenter thereof, and opposite ends which are each spaced apart from thepreset center frequency by 1 Hz, and wherein, when at least one of thesecond characteristic values is greater than a preset normal value, theprecise diagnosis unit determines that the fault is present, anddetermines a location of the fault and a kind of the fault based both ona location of a sensor that has obtained the at least one secondcharacteristic value and on the one or more preset center frequencies.14. The fault detecting system according to claim 13, wherein the one ormore preset center frequencies comprise frequencies f_(r), f_(c), f_(s),f_(o), f_(t), and a gear mesh frequency (GMF) calculated by Equations(1) to (6), wherein${{{Equation}\mspace{14mu} (1)\mspace{14mu} {is}\mspace{14mu} f_{r}} = \frac{rpm}{60}},{{{Equation}\mspace{14mu} (2)\mspace{14mu} {is}\mspace{14mu} f_{c}} = {\frac{f_{r}}{2}\left\lbrack {1 - {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}},{{{Equation}\mspace{14mu} (3)\mspace{14mu} {is}\mspace{14mu} f_{s}} = {\frac{P_{d}}{2B_{d}}{f_{r}\left\lbrack {1 - \left( {\frac{B_{d}}{P_{d}}\cos \; \varphi} \right)^{2}} \right\rbrack}}},{{{Equation}\mspace{14mu} (4)\mspace{14mu} {is}\mspace{14mu} f_{o}} = {{N\left( {F\; T\; F} \right)} = {\frac{f_{r}}{2}{N\left\lbrack {1 - {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}}}},{{{Equation}\mspace{14mu} (5)\mspace{14mu} {is}\mspace{14mu} f_{i}} = {{N\left( {f_{r} - {F\; T\; F}} \right)} = {\frac{f_{r}}{2}{N\left\lbrack {1 + {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}}}},{and}$Equation  (6)  is  G M F = (T_(R) + T_(S)) × N_(O) = (T_(R) × N_(R)) + (N_(S) × T_(S)),wherein, f_(r) is revolutions per second of a shaft, f_(c) is afundamental train frequency (FTF), f_(s) is a ball spin frequency (BSF),f_(o) is a ball pass frequency of an outer ring (BPFO), f_(t) is a ballpass frequency of an inner ring (BPFI), B_(d) is a diameter of a bearingball, P_(d) is a pitch diameter, N is a number of balls, φ is a contactangle, T_(R) is a number of teeth of a ring gear, T_(S) is a number ofteeth of a sun gear, N_(O) is revolutions per second of a carrier, N_(R)is revolutions per second of the ring gear, and N_(S) is revolutions persecond of the sun gear.
 15. The fault detecting system according toclaim 13, wherein the precise diagnosis unit extracts an envelope of thevibration data for each of the plurality of sensors, wherein the precisediagnosis unit performs a fast Fourier transform (FFT) operation basedon the envelope, wherein the precise diagnosis unit calculate, based ona result of performing the FFT, third characteristic values each ofwhich is a peak value in a frequency band which has a corresponding oneof the one or more preset center frequencies for each of the pluralityof sensors as a center thereof, and opposite ends which are each spacedapart from the preset center frequency by 1 Hz, and wherein, when atleast one of the third characteristic values is greater than a presetnormal value, the precise diagnosis unit determines that the fault ispresent, and determines a location of the fault and a kind of the faultbased both on a location of a sensor that has obtained the at least onethird characteristic value and on the preset center frequency.
 16. Thefault detecting system according to claim 15, wherein the one or morepreset center frequencies comprise frequencies f_(r), f_(c), f_(s),f_(o), f_(t), and a gear mesh frequency (GMF) calculated by Equations(1) to (6), wherein${{{Equation}\mspace{14mu} (1)\mspace{14mu} {is}\mspace{14mu} f_{r}} = \frac{rpm}{60}},{{{Equation}\mspace{14mu} (2)\mspace{14mu} {is}\mspace{14mu} f_{c}} = {\frac{f_{r}}{2}\left\lbrack {1 - {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}},{{{Equation}\mspace{14mu} (3)\mspace{14mu} {is}\mspace{14mu} f_{s}} = {\frac{P_{d}}{2B_{d}}{f_{r}\left\lbrack {1 - \left( {\frac{B_{d}}{P_{d}}\cos \; \varphi} \right)^{2}} \right\rbrack}}},{{{Equation}\mspace{14mu} (4)\mspace{14mu} {is}\mspace{14mu} f_{o}} = {{N\left( {F\; T\; F} \right)} = {\frac{f_{r}}{2}{N\left\lbrack {1 - {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}}}},{{{Equation}\mspace{14mu} (5)\mspace{14mu} {is}\mspace{14mu} f_{i}} = {{N\left( {f_{r} - {F\; T\; F}} \right)} = {\frac{f_{r}}{2}{N\left\lbrack {1 + {\frac{B_{d}}{P_{d}}\cos \; \varphi}} \right\rbrack}}}},{and}$Equation  (6)  is  G M F = (T_(R) + T_(S)) × N_(O) = (T_(R) × N_(R)) + (N_(S) × T_(S)),wherein, f_(r) is revolutions per second of a shaft, f_(c) is afundamental train frequency (FTF), f_(s) is a ball spin frequency (BSF),f_(o) is a ball pass frequency of an outer ring (BPFO), f_(t) is a ballpass frequency of an inner ring (BPFI), B_(d) is a diameter of a bearingball, P_(d) is a pitch diameter, N is a number of balls, φ is a contactangle, T_(R) is a number of teeth of a ring gear, T_(S) is a number ofteeth of a sun gear, N_(O) is revolutions per second of a carrier, N_(R)is revolutions per second of the ring gear, and N_(S) is revolutions persecond of the sun gear.
 17. The fault defect detecting system accordingto claim 11, wherein the plurality of sensors comprises a tachometer andfifteen acceleration sensors, wherein the tachometer is mounted to adriven shaft extending from the gearbox to the generator and isconfigured to measure an RPM of the driven shaft, and wherein thefifteen acceleration sensors comprise two acceleration sensorsconfigured to measure vertical and horizontal vibrations on the mainbearing, one acceleration sensor provided on each of an left end and aright end of a torque arm of the gearbox coupled with the main shaft,one acceleration sensor configured to measure vibration of a mechanicalpump bearing, one acceleration sensor configured to measure vibration ofa wheel bearing of a third gear stage of the gearbox, one accelerationsensor configured to measure vibration of a drive shaft of the thirdgear stage of the gearbox, two acceleration sensors configured tomeasure vibration of a driven shaft of the third gear stage of thegearbox, two acceleration sensors configured to measure horizontal andvertical vibrations at a side of the generator which is coupled with thegearbox so as to measure vibration of the generator, and twoacceleration sensors configured to measure horizontal and verticalvibrations at a side opposite to the side of the generator that iscoupled with the gearbox, and two acceleration sensors configured todetect front/rear direction vibration and left/right direction vibrationof the wind power generator.
 18. The fault detecting system according toclaim 15, further comprising: a display unit configured to display thefirst characteristic value, the second characteristic values, the thirdcharacteristic values, and the vibration data, wherein the display unitforms a time matrix having locations of the plurality of sensors and theone or more preset center frequencies and having the firstcharacteristic value as a value of the matrix, a frequency matrix havingthe locations of the plurality of sensors and the one or more presetcenter frequencies and having the second characteristic values as valueof the matrix, an envelope frequency matrix having the locations of theplurality of sensors and the one or more preset center frequencies andhaving the third characteristic values as values of the matrix, and avibration data matrix having the locations of the plurality of sensorsand the one or more preset center frequencies and having the receivedvibration data as a value of the matrix, and wherein the time matrix,the frequency matrix, the envelope frequency matrix, and the vibrationdata matrix are successively displayed on the display unit from a topthereof to a bottom.
 19. The fault detecting system according to claim15, further comprising: a display unit configured to display the firstcharacteristic value, the second characteristic values, and the thirdcharacteristic values, wherein the display unit if further configured todisplay: a fundamental information unit configured to display summaryinformation including information about a site where the wind powergenerator is installed and information about a time during which thevibration data is received, operation data and filter informationincluding a wind speed, a speed of the wind power generator, andproduction power of the wind power generator, and a shock finderconfigured to indicate, based on a preset filter range, the number ofthe plurality of sensors which undergo vibration corresponding to thefilter range; a gear unit including a column representing installationlocations of vibration sensor installed on gears, a part representing atleast one characteristic value of the first characteristic value, thesecond characteristic value, and the third characteristic value for eachof the vibration sensors, and a part setting a filter value; and abearing unit including a column representing installation locations ofvibration sensor installed on bearings, a part representing at least onecharacteristic value of the first characteristic value, the secondcharacteristic value, and the third characteristic value for each of thevibration sensors, and a part setting a filter value, and wherein thedisplay unit displays a characteristic value corresponding to the setfilter value with a color that is different from a color of the othercharacteristic values so as to distinguish the correspondingcharacteristic value from the other characteristic values so that a usercan recognize the location and the kind of the defect.